Context

Among all species on Earth, humans have a unique capability of communication using a symbolic communication system, i.e., verbal and written language. The highly sophisticated language enables humans to communicate in a very precise and complex manner. Still, communicative speech acts seem to differ between genders. One of the major differences in women and men’s speech is that men have been found^1,2 to dominate conversations through the use of interruptions and overlaps. Additionally, men use strong expletives, while women use politer versions.

In this project we investigate the variety of speech which is related to specific gender, social norms and variations in the use of language among those genders. We suppose men and women have different speech behaviours, women talking with more uncertainties (doubts). For example, we expect a woman to say “I expect this to do that” while a man would rather say “I know this does that”. Our idea is therefore to analyse whether there is a real difference between genders and, if so, to what extent it is the case.

Goals

We are interested in using this dataset to answer the following question:

To answer this question, we'll go through the following points:

What is our data?

In the following, we analyse the data from Quotebank, an open corpus which gathers 178 million quotations (attributed to speakers) from 2008 to 2020. Still, in this project, we will only focus on the most recent quotations, being from 2015 to 2020. We combine this dataset with speakers’ information from Wikidata, a collaboratively edited open source knowledge base.

Methods

Creation of professional & background data frames

To have a general overview of the speakers’ occupations, we focus on four main professional fields: arts, science, economy and politics. Our speakers are then regrouped under professions from each professional field. Then, to determine the roles of nationality, religion and education in determining a possible culture gender difference in communicative acts, we selected a general data frame with no condition on profession.

Classifier

To analyse speech uncertainty, we adapted an already existing uncertainty detection classifier³, using 6 features. Uncertainty is defined by speculative verbs (like, suggest, presume), adjectives and adverbs (like, probably, possibly), auxiliary verbs (like, must, should) or the use of some tense or modes of conjugation (subjunctive, conditional). This classifier is an automatic machine learning method to detect uncertainty in natural language.

Let's explore our data!

Before starting to investigate our research questions, let's have a look at what does our data look like.

Gender exploration

We see that there are 32 genders present in Wikidata. In this analysis, we will only focus on 2 genders: “Female” and “Male”.

Quote's languages

We notice that the majority of our the quotes are in English. Still, there are some non-English quotes in the dataset. These are removed from our analysis and we only keep the English ones.

Quote's female/Male Ratio

As seen from the data, a great majority of speakers are males, meaning there is a persistent under-representation of women in the news. Unfortunately, this was expected. Indeed, even today in the early twenty-first century, women continue to receive substantially less media coverage than men, despite women’s much increased participation in public life. However, only few studies have systematically examined whether such media bias exists.

Still, a recent American Sociological Review⁴ found that societal-level inequalities are the dominant determinants of continued gender differences in coverage: the media focuses nearly exclusively on the highest strata of occupational and social hierarchies, in which women’s representation has remained poor. As a result, we will focus our analysis on whether there is a difference in speech uncertainty in different professional areas, and whether those have improved from 2015 to 2020. Additionally, to broaden our analysis, the background of the speakers will also be analysed to find other possible correlations between the speakers’ environments and their speech behaviours.

Let's check out how many women are represented in the 4 profession fields we defined of interest.

Results

Analysis of differences in communicative acts in relation to gender within the different professional areas

Looking at the figure about, there seems to be only few differences in speech uncertainty between men and women, regrouping all quotes (2015 to 2020) together.

What are the roles of the speakers’ backgrounds in determining those differences in speech between men and women?

What are the roles of environment (nationality), culture/tradition (religion), and education (whether the speaker obtained an academic degree) in determining those differences in speech between men and women? How are the lines drawn between the language we use and the environment around us? Let's have a look at nationality:

Let's have a look at religion

Let's have a look at academic degree

Has there been a possible change over time (from 2015 to 2020)?

Statistical analysis

We performed linear regression using the certainty label we obtain from the Pajean uncertainty classifier to identify important features for certainty prediction. It is important to keep in mind that the classifier has a 62.8 F-score thus some of the results we obtain could be due to chance.
We considered features with p-value under 0.05 not statistically significant.

Conclusion